Index Catalog // Arch : Northwestern University Institutional Repository

Learning from Limited and Imperfect Data in Cyber-Physical System

Description:: Machine learning is seeping into every fabric in various practical domains such as autonomous driving, wearable computing, and smart buildings. However, in the actual development and integration, especially when the learning-based components are frequently included as components of large complex systems where the physical instances can be included as interactable...
Keyword:: Transfer Learning, Reinforcement Learning, Computer Vision, Building HVAC Control, Image Classification, and Machine Learning
Subject:: Computer engineering and Computer science
Creator:: Xu, Shichao
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 05/31/2023
Date Created:: 2023-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_983783 and http://dissertations.umi.com/northwestern:16519

Integrating Heterogeneous Traffic Data Sources with High Definition Maps in Autonomous Driving

Description:: Automated driving has become a very popular topic in the recent years and is becoming more and more of a reality. In this new trend, High Definition (HD) maps play an important role in many ways that will provide a safer and more efficient driving experience, especially in terms of...
Keyword:: Data Consumption, Prediction, Autonomous Driving, High Definition Maps, and Machine Learning
Subject:: Computer science
Creator:: Zang, Andi
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 06/22/2022
Date Created:: 2022-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_879487 and http://dissertations.umi.com/northwestern:15935

Model Positionality: A Novel Framework for Data Science with Subjective Target Concepts

Description:: Data Science and related fields like Artificial Intelligence, Machine Learning, and Statistics provide indispensable research methods for understanding a wide variety of phenomena from large datasets. However, as methodical and empirical as these methods aim to be, there are many subjective and discretionary choices that the data scientist must make...
Keyword:: human computation, inter-annotator agreement, algorithmic bias, content moderation, and Machine Learning
Subject:: Information science, Artificial intelligence, and Computer science
Creator:: Cambo, Scott Allen
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 06/28/2021
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_810456 and http://dissertations.umi.com/northwestern:15510

Fisher Score-Based Concept Drift Monitoring and Diagnosis with Applications to Microstructure Nonstationarity Analysis and Deep Learning Segmentation for Multiphase Materials

Description:: Supervised learning model is one of the most fundamental machine learning models. It can provide powerful capability of prediction by learning complex patterns hidden in many, sometimes thousands, predictors. It can also be used as a building block of other machine learning tasks, like unsupervised learning and reinforcement learning. Such...
Keyword:: Stochastic Microstructures, Fisher Score, Deep Learning, Machine Learning, Concept Drift, and Nonstationarity Analysis
Subject:: Materials Science, Statistics, and Computer science
Creator:: Zhang, Kungang
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/01/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_765218 and http://dissertations.umi.com/northwestern:15297

Analyzing the Role of Natural Language in Neural-Symbolic Models

Description:: The world is awash in data and much of artificial intelligence focuses on learning models of the underlying structure in this data or the mechanisms governing its evolution. Both neural and symbolic models have weaknesses that make these models sub-optimal from a use perspective. Much of this data is in...
Keyword:: Neural-Symbolic Models, Natural Language Processing, Artificial Intelligence, Neural Network Language Models, Finance Models, and Machine Learning
Subject:: Computer science
Creator:: Demeter, David
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/01/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_769746 and http://dissertations.umi.com/northwestern:15319

Computational Electron Microscopy for the Characterization of Soft, Hard, and Hybrid Materials

Description:: Connecting structure and function in nanoscale engineered materials and devices relies on the analysis of the fundamental arrangement of matter, frequently under dynamic conditions. The demand to image structures at fundamental length scales has touched inorganic materials, biology, and frequently hybrid hard/soft materials with unique phenomena driven by heterogeneous components....
Keyword:: Imaging, Materials Imaging, Soft Materials, Crystallography, Machine Learning, and Electron Microscopy
Subject:: Nanoscience, Materials Science, and Computer science
Creator:: Hujsak, Karl
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/12/2020
Date Created:: 2018-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:14515 and etdadmin_upload_638887

Generalizable Data-driven Models for Personalized Shared Control of Human-Machine Systems

Description:: The theory of how humans and machines control and communicate with each other is at the core of the scientific field known as Human-Robot Interaction (HRI). Researchers in this sub-discipline of robotics are therefore particularly interested in developing methods to chuppahreduce the inherent friction in this communication and control channel....
Keyword:: Human-Robot Interaction, Data-driven Control, Machine Learning, and Optimal Control
Subject:: Robotics and Computer science
Creator:: Broad, Alexander Sherman
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/12/2020
Date Created:: 2019-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_683518 and http://dissertations.umi.com/northwestern:14789

Mutational Processes Modeling and Early Cancer Diagnosis

Description:: Annual age-adjusted breast cancer incidence rates in the United States have been static for decades. More recently, the development of massively parallel, high throughput DNA sequencing has enabled the cataloging of somatic mutations in cancer. Mutations are non-random and occur within sequence motifs. These motifs provide us with evidence to...
Keyword:: Natural Language Processing, Electronic Health Records, Genomics, Mutational Processes, Breast Cancer, and Machine Learning
Subject:: Bioinformatics and Computer science
Creator:: Zeng, Zexian
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/12/2020
Date Created:: 2019-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:14500 and etdadmin_upload_635668

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Learning from Limited and Imperfect Data in Cyber-Physical System

Integrating Heterogeneous Traffic Data Sources with High Definition Maps in Autonomous Driving

Model Positionality: A Novel Framework for Data Science with Subjective Target Concepts

Fisher Score-Based Concept Drift Monitoring and Diagnosis with Applications to Microstructure Nonstationarity Analysis and Deep Learning Segmentation for Multiphase Materials

Analyzing the Role of Natural Language in Neural-Symbolic Models

Computational Electron Microscopy for the Characterization of Soft, Hard, and Hybrid Materials

Generalizable Data-driven Models for Personalized Shared Control of Human-Machine Systems

Mutational Processes Modeling and Early Cancer Diagnosis

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